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1Unit for Sports and Exercise Medicine, Institute of Clinical Medicine, University of Helsinki, 00250 Helsinki; 2Department of Public Health, University of Helsinki, 00014 Helsinki; 3Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, 70211 Kuopio, Finland; and 4Department of Physical Therapy, and 5Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, Canada T6G 2G4
Submitted 10 December 2003 ; accepted in final form 10 May 2004
| ABSTRACT |
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adolescents; exercise; twins; hypertension
The aim of the present study was to estimate the effects of inherited characteristics and leisure-time physical activity on BP levels among healthy men. We investigated the effects of different types of leisure-time physical activity, including physical activity in adolescence, and of genetic and nongenetic familial factors on interindividual variation of BP levels in adulthood using a sample of male twin pairs from the Finnish Twin Cohort.
| MATERIALS AND METHODS |
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Of the entire original sample, 105 MZ and 153 DZ twin pairs were examined in 19971999 when BP measurements were included. The remaining MZ pairs were examined before inclusion of BP measurements and were therefore excluded from analyses. The BP measurements of 20 subjects (from 6 MZ pairs and 13 DZ pairs) were not registered due to technical problems, such as cold fingers, amputated fingers, or arrhythmias.
Thus BP was available in 99 MZ pairs and 140 DZ pairs. We excluded those twin pairs in which at least one of the twins had diabetes (3 concordant and 3 discordant MZ pairs, and 11 discordant DZ pairs) or used medication for hypertension or other cardiovascular medication influencing BP (14 concordant and 14 discordant MZ pairs, and 11 concordant and 18 discordant DZ pairs). Some of these subjects had both aforementioned conditions. Thus there were 71 MZ pairs and 104 DZ pairs in our final study group. The mean age of the subjects was 52.0 yr (range 4072 yr) for the MZ pairs and 49.3 yr (range 4070 yr) for the DZ pairs (Table 1).
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The present study has been approved by appropriate ethics committees, and all subjects have given informed consent.
Measurements. The BP measurements were made either at 8:00 AM to 12:00 AM or at 2:00 to 5:00 PM, and the BP of both twins was always measured during the same time period after at least a 2-h fast. The previous meal was instructed to be light. The subjects had been instructed not to consume coffee or tea during the last 2 h before the measurements, and smoking was forbidden during this period. Alcohol use was restricted to a maximum of two doses the previous day, and no alcohol was allowed on the measurement day. Subjects were instructed to avoid strenuous physical activity for 1 day before the measurements. Any medications typically taken by subjects were not withheld.
After a 2-min supine rest, continuous beat-to-beat arterial BP from the middle finger was registered (Finapres BP monitor, Ohmeda, Englewood, CO) for 5 min at supine rest, while heart beats were registered (Rigel MultiCare 302, Rigel Research, Surrey, UK). The finger with the cuff was kept at heart level. Mean 5-min systolic and diastolic BP were calculated. BPs measured by the Finapres monitor have been shown to correspond well with intra-arterial brachial and radial measures at rest (7, 17, 18, 32). Finger arterial pressure measurements with a Finapres device were used because the main aim of these measurements was originally to study the function of the autonomic nervous system by recording simultaneous R-R intervals and BP fluctuations with the device.
A detailed, structured interview was conducted with each of the subjects in association with the BP measurements. The mode, frequency, intensity, mean session duration, and point of time of every regularly performed sport and exercise activity after the age of 12 yr were noted (2). The repeatability of the interviewed sport and exercise responses (on activities after the age of 12 yr) for a 5-yr test-retest interval has been reported (35). For exercise years and mean hours per week by mode for the most commonly performed exercise during one defined time period, the mean intraclass correlation coefficients (ICCs) ranged from 0.63 to 0.90, and for the sum of all lifetime exercises reported mean ICCs were 0.690.73. For exercise intensity, the repeatability was lower (mean
= 0.330.48) (35).
Lifetime history of leisure-time physical activity was classified as aerobic exercise, power-type exercise, and other exercise, and participation was summarized separately for three time periods: the whole lifetime, the past year, and adolescence (1220 yr of age). Regular aerobic exercise during the whole lifetime was classified as the number of years the subject participated in aerobic exercise on average at least two times per week and was a categorized variable (Table 1). Regular aerobic exercise during the past year was a dichotomized variable (participation at least 2 times/wk or less than that), and aerobic exercise in adolescence was classified as years of participation of at least two times per week.
The intensity of aerobic exercise was examined for the whole lifetime and the past year separately. The intensity of each type of aerobic exercise the subjects were engaged in was self-assessed by the subjects as 1) light, 2) medium, or 3) heavy. To compute whole lifetime mean intensity of aerobic exercise, the intensity was weighted by the number of years for which exercise was engaged in. For mean intensity over the preceding year, the measure was weighted by the number of months of participation. The composite scores obtained by these multiplications represent an estimated average of the experienced intensity level of the subject. The intensity of aerobic exercise variables were categorized (Table 1). The number of observations was too small for sufficient power for examining the intensity of aerobic exercise in adolescence separately. Participation in power-type exercise and other exercise were examined for each time period as dichotomous variables (Table 1).
Every job held for at least 3 mo during each subject's lifetime work history was described, and specific physical demands were noted and summarized for the whole working history and for the past year separately (3). The subjects' alcohol consumption, the history of chronic diseases, and use of medication were also noted in detail (3, 4). Age, occupational physical loading, and alcohol consumption were analyzed as categorized variables. Height and weight of the subjects were measured, and body mass index (weight/height2) was calculated and analyzed as a continuous variable.
Statistical analyses. Associations between the independent variables and the dependent variables (systolic BP and diastolic BP separately) were analyzed for all individuals by survey regression models. The sampling based on pairs and the possible within-pair correlations were taken into account (3). Those leisure-time physical activity variables that were statistically significantly associated with a dependent variable were examined by age-adjusted regression models and further by regression models adjusted for all other statistically significant variables. P values of <0.05 were regarded as significant. All significance tests were two tailed. No correction was applied for multiple statistical tests.
To estimate genetic and environmental components of variance for BP and for statistically significant leisure-time physical activity variables, standard univariate twin analyses were carried out (34, 44). Tests of homogeneity of means and variances across twin type (MZ vs. DZ) were carried out by STATA SVY procedures (StataCorp, 1999). We estimated genetic and environmental components of variance for BP and for leisure-time physical activity variables using maximum likelihood based on sample covariance matrices and means, as described in detail elsewhere (29, 34). Univariate twin models with regressors and bivariate twin models were estimated based on matrices of Pearsonian, polychoric, and biserial correlations, depending on the nature (continuous, categorized, dichotomous) of the variables (29, 34). The correlations between twin A and twin A, and twin B and twin B, respectively, are cross-trait correlations, i.e., the phenotype correlations between BP and exercise variables. The correlations between twin A and twin B are cross-trait-cross-twin correlations. Larger cross-trait-cross-twin correlations for MZ twins than for DZ twins would indicate that a genetic correlation explains part of the phenotype correlation. The univariate models are based on ICCs for MZ pairs and DZ pairs, and the bivariate models are based on the ICCs and cross-trait correlations. Twins were ordered at random with respect to birth order in the data set, and the twin A and B notation is only used to distinguish between the first and second twin in pair, respectively.
Under the present study design of twins reared together, it is possible to model four separate parameters: an additive genetic (A) component, effects due to genetic dominance (D), and shared (C) and nonshared (E) environmental components. One can fit models based on the different combinations of these parameters (e.g., AE, ACE, ADE, and E), but effects due to dominance and shared environmental effects cannot be simultaneously modeled with data limited to that from twins reared together, because the models are not identified (29, 34). We used the principle of parsimony to support accepting a simple model (e.g., AE) until evidence in support of a more complex model (e.g., ACE) requires us to abandon it. The
2 goodness-of-fit statistics were used to assess how well the models fit the data. The superiority of alternative, hierarchically nested models was assessed by Akaike information criterion (AIC;
2 2 x degrees of freedom). This was done to compare models where different components of variance have been specified. Lower AIC indicates better fit.
After the basic univariate models, specific independent variables and age were added as regressors to the BP models to evaluate how much variance each specific variable accounted for and how much of the remaining variance was then accounted for by age, genetic, and environmental components.
We also carried out a bivariate twin analysis with age correction to examine whether the genetic and environmental effects on leisure-time physical activity are correlated with the genetic and environmental effects on BP. This analysis was made for leisure-time physical-activity variables that were significant regressors in BP models. The selection of which variance components to include is based on the results of the univariate models. The analysis explores to what extent the observed correlation between a leisure-time physical-activity variable and BP (as seen in the individual-based regression models) can be accounted for by a correlation between additive genetic effects on the physical activity variable and on BP, and a correlation between the unique environmental effects of physical activity and of BP, respectively. The significance of the genetic and environmental correlations can be further tested by examining the change in fit of the bivariate model when that specific correlation is and is not included. In other words, is a genetic or environmental correlation needed to account for the observed phenotypic correlation. The analysis was carried out using a bivariate Cholesky decomposition parametrization (29, 34) and the genetic and environmental correlations computed from path coefficients as described by Neale and Cardon (29). The genetic models were estimated by using the Mx program (28).
| RESULTS |
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2 = 15.30; degrees of freedom = 13; AIC = 10.70; P = 0.29). Inclusion of shared environment (C) to the model (ACE) changed the proportion accounted for by genetic factors to 23% (95% CI: 0.00, 0.60), whereas unique environment accounted for 59% (95% CI: 0.40, 0.81), and shared environment accounted for 18% (95% CI: 0.00, 0.50). A bivariate analysis with only additive genetic and unique environmental effects (AE model) on both physical activity in adolescence and BP was done to explore whether genetic effects and unique environmental effects accounting for interindividual variability of diastolic BP on one hand and aerobic exercise in adolescence on the other hand are correlated (Table 7). The best-fitting model was one with both a genetic correlation (r = 0.27) and an environmental correlation (r = 0.18). Models with either only a genetic correlation or only an environmental correlation to account for the phenotypic association between physical activity and BP fit marginally but not statistically significantly worse. In these two reduced models, the respective correlations were statistically significant.
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| DISCUSSION |
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High-intensity aerobic exercise throughout life was also associated with low diastolic BP in adulthood. When the effect of this variable on diastolic BP was assessed together with genetic and environmental effects, it did, however, not remain significant. Power-type exercise and other exercise were not associated with diastolic BP. For systolic BP, high occupational loading was associated with low pressure, and aerobic exercise during the past year was surprisingly associated with high pressure. No other physical activity variables were associated with systolic BP, 39% of which was accounted for by genetic effects.
Several studies have demonstrated that BP is partly determined genetically (9, 25). Our present results are concordant with those of earlier studies, where the proportion of variance in BP accounted for by interindividual genetic differences ranges from 15 to 73% (8, 9, 14, 15, 16, 20, 24, 37, 39, 42).
Our result that aerobic exercise in adolescence is associated with low diastolic BP in adulthood is a new finding. Four European cohort studies have assessed subjects' physical activity at 1219 yr of age and subjects' BPs at 2040 yr of age and have not found an association between physical activity in youth and low BP in adulthood (6, 11, 23, 41). Several factors may have contributed to the seemingly conflicting findings. The subjects in the studies that did not find associations between physical activity in youth and BP in adulthood were younger at follow-up than the subjects in our present study (2040 vs. 3570 yr) (6, 11, 23, 41). Another factor that may contribute to the findings of no association can be inaccuracy in measuring physical activity and the type of exercise, especially the intensity of the activities, which is often used in calculation of total physical activity (35, 40) and perhaps may be more inaccurate in our study because the data were collected retrospectively. Also, importantly, in the aforementioned studies, physical activity in adolescence included all kinds of physical activity, aerobic exercise as well as speed- and strength-demanding activities (6, 11, 23, 41), whereas our present study examined aerobic exercise separately. One study (1) found a relationship between a positive attitude to aerobic exercise in adolescence and decreased risk for high systolic BP (diastolic BP was not reported). In that study, as in our present study, leisure-time sports activity in adolescence was not associated with low systolic BP.
Interestingly, the same genetic factors that are associated with participation in aerobic exercise in adolescence seemed to be associated with low diastolic BP in adulthood. Three different bivariate models fit the data about equally well. The ability to distinguish between different well-fitting models depends on the number of subjects in the study. Because this number is not very large in our present study, it cannot be concluded which one of the three models is the best one, and thus the general conclusion is that both genetic effects and unique environmental effects accounting for aerobic exercise in adolescence on one hand and diastolic BP on the other hand seem to be correlated.
Our finding that regular aerobic exercise during the past year was associated with high systolic BP is contradictory to earlier studies. An association between physical activity and low risk for hypertension has been found (10, 12, 30, 31, 33), as well as between physical activity and low BP (26, 43). There are no clear explanations for our present findings. One possibility is that those who have had elevated BP readings may have been advised to exercise by their physician. The negative results may also reflect the difficulties in assessing lifetime physical activity. In earlier studies (12, 13), we used questionnaires from which present metabolic equivalent hours per week were calculated and used in analyses. For all studies of the topic, obtaining a valid lifetime exercise history is a problem for which there is almost never a good practical solution. However, an in-depth interview eliciting regularly performed activities, using significant life events and stages to assist with recall, would appear to be a reasonable approach, which is supported by good response reliability of some measures as investigated with a 5-yr test-retest interval (35). Another source of concern in our study are those subjects belonging to the original cohort who were examined before BP measurements were included in the examinations and thus were excluded from the study. However, this drop out was random and thus unlikely to affect the results. Also, the history of aerobic exercise did not differ between those of the original cohort, who were included in the study, and those who were excluded (results not shown). Furthermore, in those who were excluded due to missing BP measurements, the prevalence of medication for hypertension was 14% (13 of 91 subjects), whereas it was 17% (85 of 509 subjects) in those in whom BP measurements were made.
We did find an association between high-intensity aerobic exercise and low diastolic BP. Some controversy concerning high-intensity exercise and risk for hypertension exists (10, 26, 30, 31, 33). This probably reflects difficulties in measuring intensity of exercise (35, 40).
In conclusion, aerobic exercise in adolescence and high-intensity exercise throughout life were associated with low diastolic BP in adulthood. Genetic effects partly accounted for both diastolic BP levels and aerobic exercise in adolescence, and these genetic effects seemed to be correlated.
| GRANTS |
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| ACKNOWLEDGMENTS |
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Present address of U. Kujala: Dept. of Health Sciences, Faculty of Sport and Health Sciences, University of Jyväskylä, FIN-40014 Jyväskylä, Finland.
Present address of A. Uusitalo-Koskinen: Dept. of Clinical Physiology and Nuclear Medicine, Central Finland Health Care District, FIN-40014 Jyväskylä, Finland.
| FOOTNOTES |
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The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
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